Towards the end of last year, I was lucky enough to have a short postdoc paid for by EDF Energy. The main focus of the postdoc was on looking at ways to design a competition to compare the performance of different disaggregation algorithms. This postdoc finished in January 2017 so I am not currently working on the disaggregation competition (although I strongly believe that finding a good way to compare NILM algorithms is one of the most important unsolved problems in NILM).
Very briefly: the main challenge in designing a NILM competition is getting enough clean, private testing data. It turns out that the performance of NILM algorithms can be quite inconsistent across houses: an algorithm might work well on some houses; but on other houses that same algorithm might work badly. Also, one of the promising uses of NILM is to identify “extreme” energy behaviour (such as leaving your electric oven on constantly just in case you fancy doing some baking). Identifying “extreme” behaviour is useful because users can save large sums of money with a single, simple change in behaviour. But - by definition - “extreme” behaviour is rare. Hence we need a large testing dataset (maybe 100 houses) to be confident that we’re accurately capturing the performance of each algorithm; and that each algorithm can recognise “extreme” energy behaviour. Recording this quantity of real data would be very expensive and time consuming. Hence we could consider building a high-quality simulator to generate realistic data. But this raises a whole host of additional challenges!